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-# LightRAG
+# LightRAG: Simple and Fast Retrieval-Augmented Generation
+<img src='' />

+<a href='https://github.com/HKUDS/GraphEdit'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+<a href='https://arxiv.org/abs/'><img src='https://img.shields.io/badge/arXiv--b31b1b'></a>
+
+This repository hosts the code of LightRAG. The structure of this code is based on [nano-graphrag](https://github.com/gusye1234/nano-graphrag).
+## Install
+
+* Install from source
+
+```
+cd LightRAG
+pip install -e .
+```
+* Install from PyPI
+```
+pip install lightrag-hku
+```
+
+## Quick Start
+
+* Set OpenAI API key in environment: `export OPENAI_API_KEY="sk-...".`
+* Download the demo text "A Christmas Carol by Charles Dickens" 
+```
+curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
+```
+Use the below python snippet:
+
+```
+from lightrag import LightRAG, QueryParam
+
+rag = LightRAG(working_dir="./dickens")
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Perform local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Perform global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Perform hybird search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybird")))
+```
+Batch Insert
+```
+rag.insert(["TEXT1", "TEXT2",...])
+```
+Incremental Insert
+
+```
+rag = LightRAG(working_dir="./dickens")
+
+with open("./newText.txt") as f:
+    rag.insert(f.read())
+```
+## Evaluation
+### Dataset
+The dataset used in LightRAG can be download from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
+
+### Generate Query
+LightRAG uses the following prompt to generate high-level queries, with the corresponding code located in `example/generate_query.py`.
+```
+Given the following description of a dataset:
+
+{description}
+
+Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset.
+
+Output the results in the following structure:
+- User 1: [user description]
+    - Task 1: [task description]
+        - Question 1:
+        - Question 2:
+        - Question 3:
+        - Question 4:
+        - Question 5:
+    - Task 2: [task description]
+        ...
+    - Task 5: [task description]
+- User 2: [user description]
+    ...
+- User 5: [user description]
+    ...
+```
+ 
+ ### Batch Eval
+To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
+```
+---Role---
+You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
+---Goal---
+You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. 
+
+- **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question?
+- **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question?
+- **Empowerment**: How well does the answer help the reader understand and make informed judgments about the topic?
+
+For each criterion, choose the better answer (either Answer 1 or Answer 2) and explain why. Then, select an overall winner based on these three categories.
+
+Here is the question:
+{query}
+
+Here are the two answers:
+
+**Answer 1:**
+{answer1}
+
+**Answer 2:**
+{answer2}
+
+Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion.
+
+Output your evaluation in the following JSON format:
+
+{{
+    "Comprehensiveness": {{
+        "Winner": "[Answer 1 or Answer 2]",
+        "Explanation": "[Provide explanation here]"
+    }},
+    "Empowerment": {{
+        "Winner": "[Answer 1 or Answer 2]",
+        "Explanation": "[Provide explanation here]"
+    }},
+    "Overall Winner": {{
+        "Winner": "[Answer 1 or Answer 2]",
+        "Explanation": "[Summarize why this answer is the overall winner based on the three criteria]"
+    }}
+}}
+```
+## Code Structure
+
+```
+.
+├── examples
+│   ├── batch_eval.py
+│   ├── generate_query.py
+│   ├── insert.py
+│   └── query.py
+├── lightrag
+│   ├── __init__.py
+│   ├── base.py
+│   ├── lightrag.py
+│   ├── llm.py
+│   ├── operate.py
+│   ├── prompt.py
+│   ├── storage.py
+│   └── utils.jpeg
+├── LICENSE
+├── README.md
+├── requirements.txt
+└── setup.py
+```
 ## Citation
-## Acknowledgement

-The structure of this code is based on [nano-graphrag](https://github.com/gusye1234/nano-graphrag).
+```
+@article{guo2024lightrag,
+title={LightRAG: Simple and Fast Retrieval-Augmented Generation}, 
+author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
+year={2024},
+eprint={},
+archivePrefix={arXiv},
+primaryClass={cs.IR}
+}
+```
+